Future-Query Wind Forecasting for Quadrotor Preview Control

Quadrotor control can exploit wind preview to anticipate aerodynamic disturbances, yet onboard sensing is sparse and confined to the traversed trajectory, leaving future control queries outside direct sensing support. We introduce EgoCast, a framework for turning sparse onboard observations into short-horizon wind preview at future control queries. Although these queries are unobserved, recent sensing and vehicle motion still provide structure that can be carried toward them. EgoCast therefore decomposes forecasting into a sensing-supported transported reference and a learned residual for unresolved correction and short-horizon evolution; label-anchored physics further constrains this residual where direct supervision is sparse. Under matched training, transport–residual forecasting reduces future-query RMSE by 18.5–23.6% relative to direct full-field prediction. Physical supervision further improves forecasts and benefits 21 of 25 sensing–supervision configurations across shifted observation layouts, with generally larger gains farther from historical observations. These forecast gains reduce aerodynamic force-preview RMSE by 29.1–39.3% relative to the data-only counterpart and improve closed-loop tracking. Adding EgoCast preview to DOB-MPC reduces tracking error on all four tested trajectories relative to DOB-MPC, showing that anticipatory wind preview complements reactive disturbance estimation. Together, these results establish future-query wind forecasting as a practical interface between sparse onboard sensing and preview control.